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106 results for “Habitat quality”
Data from: Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation
<p>This dataset contains the original data used in the article:</p> <p>Harásek M., Klinkovská K. & Chytrý M. (2023) Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation. <em>Applied Vegetation Science</em>, 26, e12726. https://doi.org/10.1111/avsc.12726</p> <p>The data contain plant species composition data from resurveyed vegetation plots in southwestern and central Moravia (Czech Republic). The plots were first surveyed by Milan Chytrý in 1986–1991 (“old plots”) and resurveyed by Martin Harásek, under the supervision of Milan Chytrý, in 2018–2019 (“new plots”).</p> <p>Of the old plots, 86 were sampled between 26 June and 16 September and 8 in May. Their size ranged from 5 to 49 m<sup>2</sup> (mean 33 m<sup>2</sup>). These plots were subjectively selected at different sites to document maximum variation in species composition and environmental conditions of the grasslands and heathlands studied. In each plot, all vascular plant species were recorded, and their covers were estimated using the nine-grade Braun-Blanquet scale (van der Maarel 1979). Plot locations were recorded in the form of text descriptions. Geographic coordinates of approximate location were added for each plot prior to the resurvey by the original surveyor using georeferenced aerial photographs and various information recorded in the field during the first survey, including slope, aspect and elevation. Location uncertainty (mean = 139 m) was indicated as the possible distance of the actual location from the given coordinates.</p> <p>The resurvey was conducted between 4 June and 16 August. Care was taken to select the most likely location of the original plot based on the coordinates of the approximate location, the original site description, and the occurrence of the species recorded during the first survey. New plots always had the same plot size as in the original sampling. Each old plot was resurveyed using 1–3 new plots depending on the uncertainty of the location of the old plot. A total of 94 old plots were resurveyed at 47 sites with 153 new plots. Of these, 71 old plots at 32 sites were in current protected areas, while 23 old plots at 15 sites were outside protected areas. All new plots were located using GPS with a location uncertainty of approximately 5 m.</p> <p>For each old plot resurveyed with more than one new plot, the most similar new plot (based on Bray-Curtis dissimilarity in species composition) was selected, resulting in a dataset of 94 old and 94 new plots (“best-fit dataset”). To test the robustness of the results, we created another dataset (“validation dataset”) that included the least similar of the corresponding new plots for each old plot. This dataset also included the 94 old and 94 new plots. If the old plot was resurveyed using a single new plot, that new plot was included in both the best-fit and validation datasets.</p> <p>The header data structure follows that of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, fields are added to indicate whether the new plot was used in the best-fit dataset (Best_fit) or the validation dataset (Validation). The information about location within or outside the protected area is given in the field Protection.</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_SW_moravia_acidgrass.zip</strong>. For using this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and the species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) must be installed.</li> <li>Three TXT files with columns separated by tabs: <ul> <li><strong>SW_moravia_acidgrass_species.txt</strong> contains the percentage covers of plant species in the plots, which are mid-values for cover-abundance categories of the Braun-Blanquet scale. Plant nomenclature was harmonised according to Danihelka et al. (2012).</li> <li><strong>SW_moravia_acidgrass _head.txt</strong> contains information on the number of species in each plot (number_species), the number, proportion and relative cover of threatened species (IUCN categories CR, EN, VU, NT, columns CR_NT_number, CR_NT_perc_number and CR_NT_perc_cover), alien species (alien_number, alien_perc_number, alien_perc_cover), species characteristic of dry grasslands (TH_number, TH_perc_number, TH_perc_cover), sand and rock-outcrop grasslands (TF_number, TF_perc_number, TF_perc_cover), mesotrophic grasslands (TD_number, TD_perc_number, TD_perc_cover) and herbaceous ruderal vegetation (XA_XC_number, XA_XC _perc_number, XA_XC _perc_cover) and unweighted means of Ellenberg-type indicator values for light (light), temperature (temperature), moisture (moisture), soil reaction (reaction) nutrients (nutrients) and salinity (salinity) used to test changes in these variables through time.</li> <li><strong>SW_moravia_life_forms.txt </strong>contains information about the assignment of individual species to the life form, which was used to analyse changes in frequency and cover of the life forms.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (Knollová et al. 2023; <a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
Fig. 2 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 2. Linear relationship (solid line) and 95 % confidence interval (gray area) between habitat quality predicted by the BART model (x-axis) and shell height (H in millimeters, y-axis), derived from the linear mixed model.
Fig. 4. Partial dependence plot for topographic Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 4. Partial dependence plot for topographic Fig. 5. Partial dependence plot for terrain roughness wetness index (TWI). index (tri).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o). Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 3. Partial dependence plot for BIO17 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 3. Partial dependence plot for BIO17 = Precipitation of Driest Quarter; gray area = 95 % confidence interval.
Data and JAGS-code for "Michel et al 2022 Ecology and Evolution - Reduced habitat quality increases intrinsic but not ecological costs of reproduction"
<p><strong>Abstract</strong></p> <p>Although the costs of reproduction are predicted to vary with the quality of the breeding habitat thereby affecting population dynamics and life-history trade-offs, empirical evidence for this pattern remains sparse and equivocal. Costs of reproduction can operate through immediate ecological mechanisms or through delayed intrinsic mechanisms. Ignoring these separate pathways might hinder the identification of costs and the understanding of their consequences. We experimentally investigated the survival costs of reproduction for adult little owls (<em>Athene noctua</em>) within a gradient of habitat quality. We supplemented food to nestlings, thereby relieving the parents’ effort for brood provisioning. We used radio-tracking and Bayesian multi-state modelling based on marked recapture and dead recovery to estimate survival rates of adult little owls across the year as a function of food supplementation and habitat characteristics. Food supplementation to nestlings during the breeding season increased parental survival not only during the breeding season but also during the rest of the year. Thus, the low survival of parents of unfed broods likely represents both, strong ecological and strong intrinsic costs of reproduction. However, while immediate ecological costs occurred also in high quality habitats, intrinsic costs carrying over to the post-breeding period occurred only in low quality habitats. Our results suggest that immediate costs resulting from ecological mechanisms such as predation, are high also in territories of high habitat quality. Long-term costs resulting from intrinsic trade-offs, however, are only paid in low quality habitats. Consequently, differential effects of habitat quality on immediate ecological and delayed intrinsic mechanisms can mask the increase of costs of reproduction in low quality breeding habitats. Intrinsic costs may represent an underrated mechanism of habitat quality affecting adult survival rate thereby considerably accelerating population decline in degrading habitats. This study therefore highlights the need for a long-term perspective to fully assess the costs of reproduction and the role of habitat quality in modifying these costs.</p>
Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes
<p>The data presented here are related to the research article entitled "Habitat quality does not predict animal population abundance on frequently disturbed landscapes". Using an individual-based model, we simulated movement of theoretical individuals in a dynamically disturbed landscape and quantified the error of predicting population spatial relative abundance using an habitat model. This dataset provides the Earth Mover's Distance (EMD) as prediction error measure obtained in simulations with varying individual step length and disturbance frequency.</p>
Data from: Evaluating staging habitat quality to advance the conservation of a declining migratory shorebird, Red Knot Calidris canutus
<p>Identifying where and when population "bottlenecks" occur is critical to the conservation of migratory species, many of which are declining precipitously worldwide. Especially challenging is the evaluation of changes to staging sites. These sites are indispensable links in the migratory cycle but are typically used only briefly.</p> <p>We devised a field-based approach to assess the quality and carrying capacity of a critical staging site in Nanpu, China, for the declining, migratory Red Knot (Calidris canutus rogersi & C. c. piersmai) during northward migration. The Nanpu tidal flat supports 50,000-100,000 Red Knots annually, and while there, the knots feed almost exclusively on the bivalve Potamocorbula laevis. We simultaneously monitored changes in the abundance of Red Knots and bivalves across the entire staging site in spring 2018.</p> <p>After taking into account potential competition with other shorebird species, we estimated that the Nanpu tidal flat was capable of supporting approximately 1.46-1.70 times the current observed level of usage of the site by Red Knots, and therefore is operating below, but close, to carrying capacity with respect to food resources for Red Knots. This result suggests that any further development of this site could harm the EAAF Red Knot population.</p> <p>Synthesis and applications: Quantitative monitoring and evaluation of habitat quality of staging sites are essential to successfully conserve declining migratory species. In particular, researchers and conservation practitioners should incorporate both population size and staging duration in order to more accurately assess the importance of different sites and to quantify how changes in staging habitat quality may translate into changes in migrant population sizes at both local and global scales.</p>
Habitat quality for European Eel in Agder county over time
<p>Results from method development for mapping habitat quality for European Eel</p>
Data from: Habitat quality influences pollinator pathogen prevalence through both habitat–disease and biodiversity–disease pathways
<p>The dilution effect hypothesis posits that increasing biodiversity reduces infectious disease transmission. Here, we propose that habitat quality might modulate this negative biodiversity–disease relationship. Habitat may influence pathogen prevalence directly by affecting host traits like nutrition and immune response (we coined this as the 'habitat–disease relationship') or indirectly by changing host biodiversity (biodiversity–disease relationship). We used a path model to test the relative strength of links between habitat, biodiversity, and pathogen prevalence in a pollinator–virus system. High-quality habitat metrics were directly associated with viral prevalence, providing evidence for a habitat–disease relationship. However, the strength and direction of specific habitat effects on viral prevalence varied based on the characteristics of the habitat, host, and pathogen. In general, more natural area and richness of landcover types were directly associated with increased viral prevalence, while greater floral density was associated with reduced viral prevalence. More natural habitat was also indirectly associated with reduced prevalence of two key viruses (black queen cell virus and deformed wing virus) via increased pollinator species richness, providing evidence for a habitat-mediated dilution effect on viral prevalence. Biodiversity–disease relationships varied across viruses, as prevalence of sacbrood virus was not associated with any habitat quality or pollinator community metrics. Across all viruses and hosts, habitat–disease and biodiversity–disease paths had effects of similar magnitude on viral prevalence. Therefore, habitat quality is a key driver of variation in pathogen prevalence among communities via both direct habitat–disease and indirect biodiversity–disease pathways, though the specific patterns varied among different viruses and host species. Critically, habitat–disease relationships could either contribute to or obscure dilution effects in natural systems depending on the relative strength and direction of the habitat–disease and biodiversity–disease pathways in that host–pathogen system. Therefore, habitat may be an important driver in the complex interactions between hosts and pathogens.</p>
Figure 1 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria
Figure 1. An indicative map of the sites in southern Bulgaria where P. ridibundus individuals were captured in 2019.
Figure 2 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria
Figure 2. Photos of some asymmetric P. ridibundus individuals from site 1: the Chaya River in southern Bulgaria. Legend: a–d: asymmetric morphological traits on the back of the body and hind limbs of frogs, e–f: asymmetric morphological traits on the fingers of frogs. Trait 1 – number of stripes on the dorsal side of the thigh (femur); trait 2 – number of spots on the dorsal side of the thigh; trait 3 – number of stripes on the dorsal side of the shank (crus); trait 4 – number of spots on the dorsal side of the shank; trait 5 – number of stripes on the foot (pes); trait 6 – number of spots on the foot; trait 7 – number of stripes and spots on the back (dorsum); trait 8 – number of white spots on the ventral side of the second finger of the hind leg; trait 9 – number of white spots on the ventral side of the third finger of the hind leg; trait 10 – number of white spots on the ventral side of the fourth finger of the hind leg.
Figure 11 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 11 An example of how water mites appear under a stereoscope. a – water mites of mixed taxa in a single sample ready to be sorted through and identified; b – water mites belonging to the genusTestudacarus and their easily observable characteristic dorsal plates; c – water mites belonging to the genusKongsbergia and their easily distinguishable posterior body shape.
Figure 6 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 6 The sieving process proposed in this manuscript. a – collector fills the composited sample container with water and shakes the container for approximately 10 seconds; b – immediately after shaking the collector removes the lid and pours the water through the 3mm and 250 μm sieves; c and d – the 3mm will be placed on top of the 250 μm sieve so it can filter out larger sized substrate and debris. This process is repeated 10x.
Figure 5 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 5 An example of the collector emptying the net contents from the first of four site collections into sample container; the next three site
Figure 2 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 2 Collection nets discussed in this manuscript. a – a standard, commercially available, truncated D-frame net with 500 μm mesh; b – our custom made, non-truncated D-frame net with 250 μm mesh.
Figure 1 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 1 Examples of impaired and attaining riffle-run stream habitats in central Pennsylvania. a – Warriors Mark Run, impaired stream; b – Muddy Run, impaired stream; c – Spruce Run, attaining stream; d – Laurel Run, attaining stream.
Figure 3 What a in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 3 What a single collection site should look like after the proposed three-minute collection effort. The blue arrows indicate stacked rocks that were stacked upstream of the net to better direct flow into the net. Large rocks were moved before digging into the substrate, while medium-sized and smaller rocks were placed as the substrate was dug up.
Figure 10 A in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 10 A final, picked water mite sample preserved in 80% ethanol ready for identification and enumeration.
Figure 7 After sieving, the collector removes the contents from the 250 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 7 After sieving, the collector removes the contents from the 250 μm sieve and places them into the final sample jar. a – fine sediment
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